Image-Guided, Frameless Stereotactic Sectioning of the Corpus callosum in Children with Intractable Epilepsy
Bibliographic record
Abstract
Corpus callosotomy is an effective neurosurgical procedure for children with intractable atonic or drop attack seizures. While this procedure has not changed significantly over the past three decades, some technical issues remain to be resolved. These include the intraoperative determination of the extent of the callosotomy, the need to stage the procedure, as well as side of approach of craniotomy. We report our 8-year experience with corpus callosotomy using a frameless stereotactic image-guided system (ISG Viewing Wand). Seventeen children with atonic seizures underwent sectioning of the corpus callosum. The mean patient age was 10.5 years. Six children underwent complete callosotomy while 11 underwent resection of the anterior two-thirds. MRI 3D reconstruction of the sagittal sinus and draining cerebral veins was undertaken in all cases. The side of the craniotomy was determined on the basis of favorability of the draining veins with respect to the extent of the callosotomy. The extent of the callosotomy was determined by intraoperative feedback using the ISG Viewing Wand((R)). Nine of 11 patients in the partial callosotomy group and 4 of 6 patients in the complete callosotomy group showed significant improvement in atonic seizures. We conclude that the use of frameless stereotaxy can function as an important adjunct in the planning and conduction of successful sectioning of the corpus callosum in children with intractable seizures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".